Abstract

Managing congestion effectively enables reliable and fast data transfer over networks. CUBIC delivers reliable results under normal circumstances but cannot adapt effectively to changing network scenarios. We introduce CUBIC-Learn, an RL approach for improving congestion control in CUBIC. The central idea is to use a Q-learning algorithm to adjust congestion window thresholds based on current data on packet loss, throughput, and latency. Simulations demonstrate more efficient and reliable congestion control when using CUBIC-Learn compared to standard CUBIC. CUBIC-Learn achieves a 47% reduction in packet loss, over a 59% increase in bandwidth utilization, approximately a 28% decrease in retransmissions, and 47% lower latency. In addition, CUBIC-Learn shows significant improvements in congestion window (cwnd) growth behavior, fairness among competing flows, and stability under heterogeneous traffic and network scenarios, including gigabit-scale bandwidth conditions. Statistical analysis further confirms the robustness of these gains, while the method introduces no additional computational overhead. Overall, CUBIC-Learn performs better than PCC, Reno, Tahoe, NewReno, and BBRv3 in most metrics. These findings suggest that RL can markedly improve congestion control in high-speed networks.

Key words: Q-learning, Reinforcement Learning, CUBIC Algorithm, Network Congestion